ArticleJCO clinical cancer informatics2025
Large Language Models as Decision-Making Tools in Oncology: Comparing Artificial Intelligence Suggestions and Expert Recommendations.
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- Large Language Models in Multidisciplinary Decision-Making for Hepatopancreatobiliary Oncology: Retrospective Comparative Feasibility Study.Journal of medical Internet research · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study.Journal of medical Internet research · 2026Article
- The role of artificial intelligence in precision medicine for breast cancer.Discover oncology · 2026Review
- Navigating Oral Cancer Management: Combining Artificial Intelligence and Clinical Guidelines for Optimal Decision-Making.International archives of otorhinolaryngology · 2026Article
- [Current Status of Multidisciplinary Team Consultation for Lung Cancer and Prospects under the Empowerment of Medical Large Models].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.Scientific reports · 2026Article
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- Comparing artificial intelligence and multidisciplinary tumor board decision making in real-world cancer care: a prospective blinded concordance study.ESMO real world data and digital oncology · 2026Article
- AI assistance in tumor multidisciplinary teams.ESMO real world data and digital oncology · 2026Review
- Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- Physicians' acceptance of large language model-based clinical decision support tools in gynecologic oncology: a technology acceptance model study.Frontiers in digital health · 2026Article
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
- An External Validation Study on Two Pre-Trained Large Language Models for Multimodal Prognostication in Laryngeal and Hypopharyngeal Cancer: Integrating Clinical, Treatment, and Radiomic Data to Predict Survival Outcomes with Interpretable Reasoning.Bioengineering (Basel, Switzerland) · 2025Article
- Evaluating Large Language Models in Interpreting MRI Reports and Recommending Treatment for Vestibular Schwannoma.Diagnostics (Basel, Switzerland) · 2025Article
- Accuracy of ChatGPT, Gemini, Copilot, and Claude to Blepharoplasty-Related Questions.Aesthetic plastic surgery · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine the accuracy of large language models (LLMs) in generating appropriate treatment options for patients with early breast cancer on the basis of their medical records. MATERIALS AND
methodsRetrospective study using anonymized medical records of patients with BC presented during multidisciplinary team meetings (MDTs) between January and April 2024. Three generalist artificial intelligence models (Claude3-Opus, GPT4-Turbo, and LLaMa3-70B) were used to generate treatment suggestions, which were compared with experts' decisions. The primary outcome was the rate of appropriate suggestions from the LLMs, compared with the reference experts' decisions. The secondary outcome was the LLMs' performances (F1 score and specificity) in generating appropriate suggestions for each treatment category.
resultsThe rates of appropriate suggestions were 86.6% (97/112), 85.7% (96/112), and 75.0% (84/112) for Claude3-Opus, GPT4-Turbo, and LLaMa3-70B, respectively. No significant difference was found between Claude3-Opus and GPT4-Turbo (
conclusionLLMs, particularly Claude3-Opus and GPT4-Turbo, demonstrated promising accuracy in suggesting appropriate adjuvant treatments for patients with early BC on the basis of their medical records. Although LLMs showed limitations in validating surgery and indicating genomic tests, their performance in other treatment modalities highlights their potential to automate and augment decision making during MDTs. Further studies with fine-tuned LLMs and a prospective design are needed to demonstrate their utility in clinical practice.
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